A computer-intensive "resampling" method, introduced in statistics by B. Efron in 1979 [a3] for estimating the variability of statistical quantities and for setting confidence regions (cf. also Sample; Confidence set). The name "bootstrap" refers to the analogy with pulling oneself up by one's own bootstraps. Efron's bootstrap is to resample the data. Given observations , artificial bootstrap samples are drawn with replacement from , putting equal probability mass at each . For example, with sample size and distinct observations one might obtain as bootstrap (re)sample. In fact, there are distinct bootstrap samples in this case.
A more formal description of Efron's non-parametric bootstrap in a simple setting is as follows. Suppose is a random sample of size from a population with unknown distribution function on the real line; i.e. the 's are assumed to be independent and identically distributed random variables with common distribution function (cf. also Random variable). Let denote a real-valued parameter to be estimated. Let denote an estimate of , based on the data (cf. also Statistical estimation; Statistical estimator). The object of interest is the probability distribution of ; i.e.
for all real , the exact distribution function of , properly normalized. The scaling factor is a classical one, while the centring of is by the parameter . Here denotes "probability" corresponding to .
Efron's non-parametric bootstrap estimator of is now given by
for all real . Here , where denotes an artificial random sample (the bootstrap sample) from , the empirical distribution function of the original observations , and . Note that is the random distribution (a step function) which puts probability mass at each of the 's (), sometimes referred to as the resampling distribution; denotes "probability" corresponding to , conditionally given , i.e. given the observations . Obviously, given the observed values in the sample, is completely known and (at least in principle) is also completely known. One may view as the empirical counterpart in the "bootstrap world" to in the "real world" . In practice, exact computation of is usually impossible (for a sample of distinct numbers there are distinct bootstrap (re)samples), but can be approximated by means of Monte-Carlo simulation (cf. also Monte-Carlo method). Efficient bootstrap simulation is discussed e.g. in [a2], [a10].
When does Efron's bootstrap work? The consistency of the bootstrap approximation , viewed as an estimate of , i.e. one requires
to hold, in -probability, is generally viewed as an absolute prerequisite for Efron's bootstrap to work in the problem at hand. Of course, bootstrap consistency is only a first-order asymptotic result and the error committed when is estimated by may still be quite large in finite samples. Second-order asymptotics (Edgeworth expansions; cf. also Edgeworth series) enables one to investigate the speed at which approaches zero, and also to identify cases where the rate of convergence is faster than , the classical Berry–Esseen-type rate for the normal approximation. An example in which the bootstrap possesses the beneficial property of being more accurate than the traditional normal approximation is the Student -statistic and more generally Studentized statistics. For this reason the use of bootstrapped Studentized statistics for setting confidence intervals is strongly advocated in a number of important problems. A general reference is [a7].
When does the bootstrap fail? It has been proved [a1] that in the case of the mean, Efron's bootstrap fails when is the domain of attraction of an -stable law with (cf. also Attraction domain of a stable distribution). However, by resampling from , with (smaller) resample size , satisfying and , it can be shown that the (modified) bootstrap works. More generally, in recent years the importance of a proper choice of the resampling distribution has become clear, see e.g. [a5], [a9], [a10].
The bootstrap can be an effective tool in many problems of statistical inference; e.g. the construction of a confidence band in non-parametric regression, testing for the number of modes of a density, or the calibration of confidence bounds, see e.g. [a2], [a4], [a8]. Resampling methods for dependent data, such as the "block bootstrap" , is another important topic of recent research, see e.g. [a2], [a6].
|[a1]||K.B. Athreya, "Bootstrap of the mean in the infinite variance case" Ann. Statist. , 15 (1987) pp. 724–731|
|[a2]||A.C. Davison, D.V. Hinkley, "Bootstrap methods and their application" , Cambridge Univ. Press (1997)|
|[a3]||B. Efron, "Bootstrap methods: another look at the jackknife" Ann. Statist. , 7 (1979) pp. 1–26|
|[a4]||B. Efron, R.J. Tibshirani, "An introduction to the bootstrap" , Chapman&Hall (1993)|
|[a5]||E. Giné, "Lectures on some aspects of the bootstrap" P. Bernard (ed.) , Ecole d'Eté de Probab. Saint Flour XXVI-1996 , Lecture Notes Math. , 1665 , Springer (1997)|
|[a6]||F. Götze, H.R. Künsch, "Second order correctness of the blockwise bootstrap for stationary observations" Ann. Statist. , 24 (1996) pp. 1914–1933|
|[a7]||P. Hall, "The bootstrap and Edgeworth expansion" , Springer (1992)|
|[a8]||E. Mammen, "When does bootstrap work? Asymptotic results and simulations" , Lecture Notes Statist. , 77 , Springer (1992)|
|[a9]||H. Putter, W.R. van Zwet, "Resampling: consistency of substitution estimators" Ann. Statist. , 24 (1996) pp. 2297–2318|
|[a10]||J. Shao, D. Tu, "The jackknife and bootstrap" , Springer (1995)|
Bootstrap method. Encyclopedia of Mathematics. URL: http://encyclopediaofmath.org/index.php?title=Bootstrap_method&oldid=11753